Maintenance-Free Smart Hand Dynamometer.

Grip strength measurement is one of the most accessible methods for measuring overall muscle strength, and many studies have shown a link between low grip strength and future diseases. In recent years, devices for grip strength measurements that can connect to digital devices for automatic data recording have been developed. However, such devices have high development costs and require daily maintenance. Therefore, this we propose a grip strength measurement method using the capacitance sensor of a smartphone and no electronic parts on the measurement device side.

Diagnosis of carpal tunnel syndrome using a 10-s grip-and-release test with video and machine learning analysis.

We developed a finger motion-based diagnostic system for carpal tunnel syndrome by analysing 10 second grip-and-release test videos. Using machine learning, it estimated presence of carpal tunnel syndrome (89% sensitivity and 83% specificity) and correlated with severity on nerve conduction studies (coefficient 0.68). LEVEL OF EVIDENCE: III.

Screening for degenerative cervical myelopathy with the 10-second grip-and-release test using a smartphone and machine learning: A pilot study.

Objective: Early detection and intervention are essential for the mitigation of degenerative cervical myelopathy (DCM). However, although several screening methods exist, they are difficult to understand for community-dwelling people, and the equipment required to set up the test environment is expensive. This study investigated the viability of a DCM-screening method based on the 10-second grip-and-release test using a machine learning algorithm and a smartphone equipped with a camera to facilitate a simple screening system. Methods: Twenty-two participants comprising a group of DCM patients and 17 comprising a control group participated in this study. A spine surgeon diagnosed the presence of DCM. Patients performing the 10-second grip-and-release test were filmed, and the videos were analyzed. The probability of the presence of DCM was estimated using a support vector machine algorithm, and sensitivity, specificity, and area under the curve (AUC) were calculated. Two assessments of the correlation between estimated scores were conducted. The first used a random forest regression model and the Japanese Orthopaedic Association scores for cervical myelopathy (C-JOA). The second assessment used a different model, random forest regression, and the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire. Results: The final classification model had a sensitivity of 90.9%, specificity of 88.2%, and AUC of 0.93. The correlations between each estimated score and the C-JOA and DASH scores were 0.79 and 0.67, respectively. Conclusions: The proposed model could be a helpful screening tool for DCM as it showed excellent performance and high usability for community-dwelling people and non-spine surgeons.

A screening method for cervical myelopathy using machine learning to analyze a drawing behavior.

Early detection of cervical myelopathy (CM) is important for a favorable outcome, as its prognosis is poor when left untreated. We developed a screening method for CM using machine learning-based analysis of the drawing behavior of 38 patients with CM and 66 healthy volunteers. Using a stylus pen, the participants traced three different shapes displayed on a tablet device. During the tasks, writing behaviors, such as the coordinates, velocity, and pressure of the stylus tip, along with the drawing time, were recorded. From these data, features related to the drawing pressure, and time to trace each shape and combination of shapes were used as training data for the support vector machine, a machine learning algorithm. To evaluate the accuracy, a receiver operating characteristic curve was generated, and the area under the curve (AUC) was calculated. Models with triangular waveforms tended to be the most accurate. The best triangular wave model identified patients with and without CM with 76% sensitivity and 76% specificity, yielding an AUC of 0.80. Our model was able to classify CM with high accuracy and could be applied to the development of disease screening systems useful outside the hospital setting.

Development and testing of a new application for measuring motion at the cervical spine.

BACKGROUND: Cervical myelopathy is a progressive disease, and early detection and treatment contribute to prognosis. Evaluation of cervical intervertebral instability by simple X-ray is used in clinical setting and the information about instability is important to understand the cause of myelopathy, but evaluation of the intervertebral instability by X-ray is complicated. To reduce the burden of clinicians, a system that automatically measures the range of motion was developed by comparing the flexed and extended positions in the lateral view of a simple X-ray of the cervical spine. The accuracy of the system was verified by comparison with spine surgeons and residents to determine whether the system could withstand actual use. METHODS: An algorithm was created to recognize the four corners of the vertebral bodies in a lateral cervical spine X-ray image, and a system was constructed to automatically measure the range of motion between each vertebra by comparing X-ray images of the cervical spine in extension and flexion. Two experienced spine surgeons and two residents performed the study on the remaining 23 cases. Cervical spine range of motion was measured manually on X-ray images and compared with automatic measurement by this system. RESULTS: Of a total of 322 cervical vertebrae in 46 images, 313 (97%) were successfully estimated by our learning model. The mean intersection over union value for all the 46-test data was 0.85. The results of measuring the CRoM angle with the proposed cervical spine motion angle measurement system showed that the mean error from the true value was 3.5° and the standard deviation was 2.8°. The average standard deviations for each measurement by specialist and residents are 2.9° and 3.2°. CONCLUSIONS: A system for measuring cervical spine range of motion on X-ray images was constructed and showed accuracy comparable to that of spine surgeons. This system will be effective in reducing the burden on and saving time of orthopedic surgeons by avoiding manually measuring X-ray images. Trial registration Retrospectively registered with opt-out agreement.

Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor.

STUDY DESIGN: Cross-sectional study. OBJECTIVE: To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA), a novel noncontact sensor device. SUMMARY OF BACKGROUND DATA: Progress of CM symptoms are gradual and cannot be easily identified by the patients themselves. Therefore, screening methods should be developed for patients of CM before deterioration of myelopathy. Although some studies have been conducted to objectively evaluate hand movements specific to myelopathy using cameras or wearable sensors, their methods are unsuitable for simple screening outside hospitals because of the difficulty in obtaining and installing their equipment and the long examination time. METHODS: In total, 50 and 28 participants in the CM and control groups were recruited, respectively. The diagnosis of CM was made by spine surgeons. We developed a desktop system using Leap Motion that recorded 35 parameters of fingertip movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used to develop the binary classification model, and a multiple linear regression analysis was performed to create regression models to estimate the total Japanese Orthopaedic Association (JOA) score and the JOA score of the motor function of the upper extremity (MU-JOA score). RESULTS: The binary classification model indexes were as follows: sensitivity, 84.0%; specificity, 60.7%; accuracy, 75.6%; area under the curve, 0.85. The Spearman rank correlation coefficient between the estimated score and the total JOA score was 0.44 and that between the estimated score and the MU-JOA score was 0.51. CONCLUSION: Our binary classification model using a machine learning algorithm and Leap Motion could classify CM with high sensitivity and would be useful for CM screening in daily life before consulting doctors and telemedicine.Level of Evidence: 3.

The Accuracy of a Screening System for Carpal Tunnel Syndrome Using Hand Drawing.

When carpal tunnel syndrome (CTS), an entrapment neuropathy, becomes severe, thumb motion is reduced, which affects manual dexterity, such as causing difficulties in writing; therefore, early detection of CTS by screening is desirable. To develop a screening method for CTS, we developed a tablet app to measure the stylus trajectory and pressure of the stylus tip when drawing a spiral on a tablet screen using a stylus and, subsequently, used these data as training data to predict the classification of participants as non-CTS or CTS patients using a support vector machine. We recruited 33 patients with CTS and 31 healthy volunteers for this study. From our results, non-CTS and CTS were classified by our screening method with 82% sensitivity and 71% specificity. Our CTS screening method can facilitate the screening for potential patients with CTS and provide a quantitative assessment of CTS.

Optimizing Sensor Position with Virtual Sensors in Human Activity Recognition System Design.

Human activity recognition (HAR) systems combined with machine learning normally serve users based on a fixed sensor position interface. Variations in the installing position will alter the performance of the recognition and will require a new training dataset. Therefore, we need to understand the role of sensor position in HAR system design to optimize its effect. In this paper, we designed an optimization scheme with virtual sensor data for the HAR system. The system is able to generate the optimal sensor position from all possible locations under a given sensor number. Utilizing virtual sensor data, the training dataset can be accessed at low cost. The system can help the decision-making process of sensor position selection with great accuracy using feedback, as well as output the classifier at a lower cost than a conventional training model.

A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study.

BACKGROUND: Carpal tunnel syndrome (CTS) is a medical condition caused by compression of the median nerve in the carpal tunnel due to aging or overuse of the hand. The symptoms include numbness of the fingers and atrophy of the thenar muscle. Thenar atrophy recovers slowly postoperatively; therefore, early diagnosis and surgery are important. While physical examinations and nerve conduction studies are used to diagnose CTS, problems with the diagnostic ability and equipment, respectively, exist. Despite research on a CTS-screening app that uses a tablet and machine learning, problems with the usage rate of tablets and data collection for machine learning remain. OBJECTIVE: To make data collection for machine learning easier and more available, we developed a screening app for CTS using a smartphone and an anomaly detection algorithm, aiming to examine our system as a useful screening tool for CTS. METHODS: In total, 36 participants were recruited, comprising 36 hands with CTS and 27 hands without CTS. Participants controlled the character in our app using their thumbs. We recorded the position of the thumbs and time; generated screening models that classified CTS and non-CTS using anomaly detection and an autoencoder; and calculated the sensitivity, specificity, and area under the curve (AUC). RESULTS: Participants with and without CTS were classified with 94% sensitivity, 67% specificity, and an AUC of 0.86. When dividing the data by direction, the model with data in the same direction as the thumb opposition had the highest AUC of 0.99, 92% sensitivity, and 100% specificity. CONCLUSIONS: Our app could reveal the difficulty of thumb opposition for patients with CTS and screen for CTS with high sensitivity and specificity. The app is highly accessible because of the use of smartphones and can be easily enhanced by anomaly detection.

Development and User Evaluation of a Smartphone-Based System to Assess Range of Motion of Wrist Joint.

PURPOSE: Measuring range of motion (ROM) in the wrist joint is an essential part of hand and wrist functional evaluations, especially before and after surgery. However, accurate measurements require experience and time. To reduce patient and surgeon burdens related to ROM measurement, a smartphone-based system, which enables participants to measure the ROM of the wrist joint semiautomatically using self-taken pictures on a smartphone, was developed and evaluated in this study. METHODS: In the developed system, participants were asked to take a picture of their wrist by using the other hand to position the joint first into full flexion and then into full extension. The hand and arm regions were automatically extracted in the program, and the ROM was estimated after the area of the hand and forearm was cropped. To verify the accuracy of ROM measurements in this system, the proposed method was tested on 66 images of hands from 33 participants; measurements were compared with those taken by hand surgeons. A limit of agreement and an intraclass correlation coefficient (ICC) were used for evaluation. RESULTS: The smallest averages (95% limits of agreement) of flexion and extension were 11.32° (95% confidence interval [CI], 8.88° to 13.76°) and 11.01° (95% CI, 8.64° to 13.39°), respectively. The ICC (1,1) for 3 measurements taken by one assessor was 0.99 (95% CI, 0.986-0.992), and the ICC (2,1) for 2 measurements taken by both assessors was 0.97 (95% CI, 0.947-0.977). CONCLUSIONS: In this study, we developed a system to measure the semiautomatic ROM of the wrist joint using a smartphone image. Its accuracy was within a clinically usable error range that was comparable with that of a hand surgeon. CLINICAL RELEVANCE: This system can reduce the burden of ROM measurement for both patients and doctors.

Myristate can be used as a carbon and energy source for the asymbiotic growth of arbuscular mycorrhizal fungi.

Arbuscular mycorrhizal (AM) fungi, forming symbiotic associations with land plants, are obligate symbionts that cannot complete their natural life cycle without a host. The fatty acid auxotrophy of AM fungi is supported by recent studies showing that lipids synthesized by the host plants are transferred to the fungi, and that the latter lack genes encoding cytosolic fatty acid synthases. Therefore, to establish an asymbiotic cultivation system for AM fungi, we tried to identify the fatty acids that could promote biomass production. To determine whether AM fungi can grow on medium supplied with fatty acids or lipids under asymbiotic conditions, we tested eight saturated or unsaturated fatty acids (C12 to C18) and two ß-monoacylglycerols. Only myristate (C14:0) led to an increase in the biomass of Rhizophagus irregularis, inducing extensive hyphal growth and formation of infection-competent secondary spores. However, such spores were smaller than those generated symbiotically. Furthermore, we demonstrated that R. irregularis can take up fatty acids in its branched hyphae and use myristate as a carbon and energy source. Myristate also promoted the growth of Rhizophagus clarus and Gigaspora margarita Finally, mixtures of myristate and palmitate accelerated fungal growth and induced a substantial change in fatty acid composition of triacylglycerol compared with single myristate application, although palmitate was not used as a carbon source for cell wall biosynthesis in this culture system. Our findings demonstrate that myristate boosts the asymbiotic growth of AM fungi and can also serve as a carbon and energy source.

Gait Measurement at Home Using A Single RGB Camera.

BACKGROUND: An individual's gait is a key factor for consideration in evaluating their overall health. Several medical studies have demonstrated the correlation between gait and incidence rate of diseases, mortality, and risk of fall. However, gait is only occasionally evaluated during medical visits, which may delay the detection of health problems. METHODS: In this paper, we propose a gait measurement system that is suitable for use at home. Our method requires only a single RGB camera, whereas other visionary sensor-based methods require depth sensors or multiple RGB cameras. In addition, the setup for the measurement is easy. What the user has to do is only putting a single camera in a room and choosing four location known points on the floor. Our method can measure step positions and step timings, and therefore, other important parameters such as stride length, step width, walking speed, and cadence may also be captured. The individual's gait is captured by the camera, and therefore the user is not required to wear any devices. RESULTS: In the experiment described herein, we demonstrate our method's accuracy by comparing it with the motion capture system. The results indicate that our method can measure walking speed with an error of 3.62 cm/s from the side view, and which is too small a change to be clinically meaningful.

A Tablet-Based App for Carpal Tunnel Syndrome Screening: Diagnostic Case-Control Study.

BACKGROUND: Carpal tunnel syndrome (CTS), the most common neuropathy, is caused by a compression of the median nerve in the carpal tunnel and is related to aging. The initial symptom is numbness and pain of the median nerve distributed in the hand area, while thenar muscle atrophy occurs in advanced stages. This atrophy causes failure of thumb motion and results in clumsiness; even after surgery, thenar atrophy does not recover for an extended period. Medical examination and electrophysiological testing are useful to diagnose CTS; however, visits to the doctor tend to be delayed because patients neglect the symptom of numbness in the hand. To avoid thenar atrophy-related clumsiness, early detection of CTS is important. OBJECTIVE: To establish a CTS screening system without medical examination, we have developed a tablet-based CTS detection system, focusing on movement of the thumb in CTS patients; we examined the accuracy of this screening system. METHODS: A total of 22 female CTS patients, involving 29 hands, and 11 female non-CTS participants were recruited. The diagnosis of CTS was made by hand surgeons based on electrophysiological testing. We developed an iPad-based app that recorded the speed and timing of thumb movements while playing a short game. A support vector machine (SVM) learning algorithm was then used by comparing the thumb movements in each direction among CTS and non-CTS groups with leave-one-out cross-validation; with this, we conducted screening for CTS in real time. RESULTS: The maximum speed of thumb movements between CTS and non-CTS groups in each direction did not show any statistically significant difference. The CTS group showed significantly slower average thumb movement speed in the 3 and 6 o'clock directions (P=.03 and P=.005, respectively). The CTS group also took a significantly longer time to reach the points in the 2, 3, 4, 5, 6, 8, 9, and 11 o'clock directions (P<.05). Cross-validation revealed that 27 of 29 CTS hands (93%) were classified as having CTS, while 2 of 29 CTS hands (7%) did not have CTS. CTS and non-CTS were classified with 93% sensitivity and 73% specificity. CONCLUSIONS: Our newly developed app could classify disturbance of thumb opposition movement and could be useful as a screening test for CTS patients. Outside of the clinic, this app might be able to detect middle-to-severe-stage CTS and prompt these patients to visit a hand surgery specialist; this may also lead to medical cost-savings.

RoboJockey: Designing an Entertainment Experience with Robots.

The RoboJockey entertainment system consists of a multitouch tabletop interface for multiuser collaboration. RoboJockey enables a user to choreograph a mobile robot or a humanoid robot by using a simple visual language. With RoboJockey, a user can coordinate the mobile robot's actions with a combination of back, forward, and rotating movements and coordinate the humanoid robot's actions with a combination of arm and leg movements. Every action is automatically performed to background music. RoboJockey was demonstrated to the public during two pilot studies, and the authors observed users' behavior. Here, they report the results of their observations and discuss the RoboJockey entertainment experience.